Introduction

Creating an R Markdown file

Useful resources to refer: https://rmarkdown.rstudio.com/authoring_quick_tour.html

ggplot introduction

R graph library: https://rmarkdown.rstudio.com/authoring_quick_tour.html
Why ggplot2? ggplot2 is an R package dedicated to data visualization. It can greatly improve the quality and aesthetics of your graphics, and will make you much more efficient in creating them. ggplot2 allows to build almost any type of chart.

File Management

create a project: organizes all files (data, output, scripts) related to a specific analysis within a dedicated folder, automatically setting the working directory to that folder when you open the project
you can set working directory: setting a working directory simply specifies the current location where R will look for files to read or save, without necessarily creating a structured project folder

#setwd("path/to/project")

Part1: Visualizing tidy data w/ `ggplot`

Reviewing the basics of the Grammar of Graphics

Three main components:

data ggplot()
- Tells ggplot which data object to use
- data.frame
- tibble
aesthetics aes() aes =
- Tells ggplot to know how to treat your data.
- x
- y
- can relate to parts of your data
  - color (for lines, points, borders)
  - fill (for geometries that have borders and the inside can be another color)
  - size (AKA ‘cex’ in base R)
  - shape (AKA ‘pch’ in base R)
  - lty (linetype)
- style (not typically linked to data)
  - lwd (line width)
  - alpha (transparency)
- extras (e.g. positioning text labels)
  - vjust (relative vertical position from 0 to 1)
  - hjust (relative horizontal position from 0 to 1)
geometry geom_...
- geom_point
- geom_line
- geom_bar
- geom_boxplot
- geom_violin
- geom_histogram
- geom_density
- geom_area
- geom_smooth
- extras for labeling
  - geom_text
  - geom_title
- extras for other purposes
  - geom_abline
- note that aesthetics can be part of geometry, too.

Basic plotting

First, load packages we’ll be using.

# most simple to load the whole tidyverse
  library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

# if you're having trouble, these shuold be all you need:
  #library(ggplot2)
  #library(tidyr)

Next, load the data we’ll be working with today (courtesy of Janani Ravi and Arjun Krishnan).

# load the table in the data folder
  gene_loc <- read.table("GSE69360.gene-locations.txt", 
                    header = T)

Quick inspection of the dataset.

# get column names
  colnames(gene_loc)

## [1] "Geneid" "Chr"    "Start"  "End"    "Strand" "Length"

# see first 5 rows
  head(gene_loc)

##            Geneid  Chr Start   End Strand Length
## 1 ENSG00000223972 chr1 11869 12227      +   1756
## 2 ENSG00000227232 chr1 14363 14829      -   2073
## 3 ENSG00000243485 chr1 29554 30039      +   1021
## 4 ENSG00000237613 chr1 34554 35174      -   1219
## 5 ENSG00000268020 chr1 52473 53312      +    947
## 6 ENSG00000240361 chr1 62948 63887      +    940

# see last 5 rows
  tail(gene_loc)

##                Geneid  Chr Start   End Strand Length
## 57815 ENSG00000198786 chrM 12337 14148      +   1812
## 57816 ENSG00000198695 chrM 14149 14673      -    525
## 57817 ENSG00000210194 chrM 14674 14742      -     69
## 57818 ENSG00000198727 chrM 14747 15887      +   1141
## 57819 ENSG00000210195 chrM 15888 15953      +     66
## 57820 ENSG00000210196 chrM 15956 16023      -     68

Creating a plot w/ Grammar of Graphics

Barplots & Histograms

ggplot, factor, aes
geom_bar, geom_histogram
facet_wrap
scale_x_log10, labs, coord_flip, theme, theme_minimal

gene_loc %>%                              # data
  ggplot(aes(x = Chr)) +                  # aesthetics: what to plot?
  geom_bar()                              # geometry: how to plot?

This looks messy. The names are overlapping, and the order is not correct. Let’s try making some adjustments.

# convert the chromosome names to factors
gene_loc$Chr <- factor(gene_loc$Chr,
                       levels = paste("chr",
                                      c((1:22), "X", "Y", "M"),
                                      sep=""))

# remember that you can assign names to ggplot objects
plot_chr_numgenes <- gene_loc %>%
                        ggplot(aes(x = Chr)) +
                        geom_bar()
plot_chr_numgenes

Still hard to read, so let’s try rotating the axes.

plot_chr_numgenes +      # load the previously-named ggplot object
  coord_flip() +         # rotate the axes
  theme_minimal()        # change to a white background

Let’s say we want to the order to be reversed. We can also do that here.

plot_chr_numgenes +      
  coord_flip() +         
  theme_minimal() +       
  scale_x_discrete(limits = rev(levels(gene_loc$Chr))) # change the order

Label the axes and assign a title

plot_chr_numgenes +
  labs(title = "No. genes per chromosome",
       x = "Chromosome",
       y = "No. of genes") +
  theme_minimal() +
  coord_flip()+       
  scale_x_discrete(limits = rev(levels(gene_loc$Chr)))

We can also make other geometries, such as a histogram.

gene_loc %>%
  ggplot(aes(x = Length)) +
  geom_histogram(color = "white") + # color here is the  
  scale_x_log10() +                 # put the histogram on the log scale
  theme_minimal()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Next, we can separate the histograms into facets my chromosome name.

# Create a facet grid using `facet_wrap()`.
plot_chr_genelength <- gene_loc %>%              # assign a new ggplot object
                          ggplot(aes(x = Length, fill = Chr)) +
                          geom_histogram(color = "white") +
                          scale_x_log10() +
                          theme_minimal() +
                          facet_wrap(~Chr, #sort facet by these data levels
                                     scales = "free_y") #only y-axis can vary
# view plot
plot_chr_genelength

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Because each facet grid is already labeled, we do not need to have a legend. We also can rotate the axes using theme() and element_text().

# remove legend and add labels
plot_chr_genelength +
  theme(legend.position = "none") +
  labs(x = "Gene length (log-scale)",
       y = "No. of genes") +
  theme(axis.text.x = element_text(angle = 45, hjust = .75))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

A little more about aesthetics: Colors!

Global versus data-level color scales

Notice that colors can be changed globally (i.e. all one color), or at the data level (factors, values).

For example:

fill here is at the data level (each facet has its own color).

gene_loc %>%
  ggplot(aes(x = Length, fill = Chr)) + # FILL is at the DATA level
  geom_histogram(color = "white") +
  scale_x_log10() +
  theme_minimal() +
  facet_wrap(~Chr, scales = "free_y") +
  theme(legend.position = "none") +
  labs(x = "Gene length (log-scale)",
       y = "No. of genes") +
  theme(axis.text.x = element_text(angle = 45, hjust = .75))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

fill here is at the global level (all facets colored the same).

gene_loc %>%
  ggplot(aes(x = Length)) + # FILL at the DATA level is REMOVED
  geom_histogram(color = "white", fill="blue") + # FILL is now GLOBAL
  scale_x_log10() +
  theme_minimal() +
  facet_wrap(~Chr, scales = "free_y") +
  theme(legend.position = "none") +
  labs(x = "Gene length (log-scale)",
       y = "No. of genes") +
  theme(axis.text.x = element_text(angle = 45, hjust = .75))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Customized color scales

You can use scales to customize your colors. Or, you can use them to match the level or categorical feature you mention in aes(fill=...) or aes(color=...). In this sense, scales “talk” to the aesthetics functions and work hand-in-hand.

Here are some to consider:

scale_color_manual/scale_fill_manual (colorize by levels in the data)
scale_color_discrete/scale_fill_discrete (categorical values)
scale_color_gradientn/scale_fill_gradientn (gradient of n colors)
scale_color_gradient/scale_fill_gradient (2-color gradient; low-high)
scale_color_gradient/scale_fill_gradient (diverging 2-color gradient; low-mid-high)
scale_color_colorblind/scale_fill_colorblind (up to 8 colorblind-friendly colors)
scale_color_brewer/scale_fill_brewer (based on color brewer)
scale_color_grey/scale_fill_grey (grey colors)

To use them, you need color palettes, such as these below:

Color palettes from http://colorbrewer2.org (made by a geography student at MSU!)

# load colorbrewer library
library(RColorBrewer)

Check out the colors that they offer.

# show all colors
  display.brewer.all()

You can change the number according to how many colors you’d like to use to see what’s available.

# show side-by-side
  par(mfrow=c(1,2))

# show 3 colors
  display.brewer.all(3)
  
# show for 10 colors
  display.brewer.all(10)

Quick example using scale_fill_manual():

# Because we have more data levels than the colorbrewer colors, make a palette
num_cols <- length(levels(gene_loc$Chr)) #get number of colors needed
newcolors <- colorRampPalette(brewer.pal(10, "BrBG"))(num_cols) #select palette

# make a new ggplot object with the basics (going to use later)
num_genes_chr <- gene_loc %>%
        ggplot(aes(x = Length, fill = Chr)) + # FILL is at the DATA level
        geom_histogram(color = "white") +
        scale_x_log10() +
        theme_minimal() +
        facet_wrap(~Chr, scales = "free_y") +
        theme(legend.position = "none") +
        labs(x = "Gene length (log-scale)",
             y = "No. of genes") +
        theme(axis.text.x = element_text(angle = 45, hjust = .75))
  
# add custom colors
num_genes_chr +
  scale_fill_manual(values = newcolors) # try values=rev(newcolors) too!

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Wes Anderson palettes, from the tumbler blog, https://wesandersonpalettes.tumblr.com/.

# load library
  library(wesanderson)

# get names of movie color themes
  names(wes_palettes)

##  [1] "BottleRocket1"     "BottleRocket2"     "Rushmore1"        
##  [4] "Rushmore"          "Royal1"            "Royal2"           
##  [7] "Zissou1"           "Zissou1Continuous" "Darjeeling1"      
## [10] "Darjeeling2"       "Chevalier1"        "FantasticFox1"    
## [13] "Moonrise1"         "Moonrise2"         "Moonrise3"        
## [16] "Cavalcanti1"       "GrandBudapest1"    "GrandBudapest2"   
## [19] "IsleofDogs1"       "IsleofDogs2"       "FrenchDispatch"   
## [22] "AsteroidCity1"     "AsteroidCity2"     "AsteroidCity3"

# show side-by-side
  par(mfrow=c(3,3))

# print out a few to see what they look like
  wes_palette("FantasticFox1")    #Fantastic Mr. Fox (2009)
  wes_palette("Zissou1")          #The Life Aquatic with Steve Zissou (2004)
  wes_palette("GrandBudapest1")   #The Grand Budapest Hotel (2014)
  wes_palette("GrandBudapest2")   #The Grand Budapest Hotel (2014)
  wes_palette("Darjeeling1")      #The Darjeeling Limited (2007)
  wes_palette("Darjeeling2")      #The Darjeeling Limited (2007)
  wes_palette("Royal1")           #The Royal Tenenbaums (2001)
  wes_palette("Royal2")           #The Royal Tenenbaums (2001)
  wes_palette("Moonrise1")        #Moonrise Kingdom (2012)

Quick example using one of these:

newcolors <- wes_palette(name = "Zissou1", #select one of the color names
                         n = num_cols,
                         type = "continuous") #as opposed to "discrete"

# add custom colors
num_genes_chr +
  scale_fill_manual(values = newcolors)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# another way to do it is within scale_fill_manual
num_genes_chr +
  scale_fill_manual(values = wes_palette(n=num_cols, name = 'Darjeeling1', type = "continuous"))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Other color sources to check out

Color-blind-friendly colors from Paul Tol: https://personal.sron.nl/~pault/data/colourschemes.pdf

R color cheatsheet: https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf

Viridis color library:

#install.packages("viridis")
#library(viridis)

Part2: Arrange multiple ggplot objects on a page

library(ggpubr)
library(ggrepel)

scatter <- ggplot(gene_loc, aes(x=End-Start, y=Length, group=Chr, color=Chr)) +
  geom_point()
scatter

It is hard to visualize the entire data.
Let’s pretend we are only interested in a small set of chromosomes.
Let’s subset the data and add a few variables!

target <- c("chrX", "chrY", "chrM", "chr17")
gene_loc2 <- filter(gene_loc, Chr %in% target)

log_EndStart <- log10(gene_loc2$End-gene_loc2$Start)
log_length <- log10(gene_loc2$Length)
gene_loc2$log_length <- log_length
gene_loc2$log_EndStart <- log_EndStart
head(gene_loc2)

##            Geneid   Chr  Start    End Strand Length log_length log_EndStart
## 1 ENSG00000273288 chr17   4961   5048      -     88   1.944483     1.939519
## 2 ENSG00000272636 chr17   5810   6168      -   1480   3.170262     2.553883
## 3 ENSG00000273172 chr17  33615  34249      -   1185   3.073718     2.802089
## 4 ENSG00000181031 chr17  62293  63714      -   5953   3.774736     3.152594
## 5 ENSG00000262920 chr17 171183 171422      +    432   2.635484     2.378398
## 6 ENSG00000262061 chr17 180996 183279      +   2284   3.358696     3.358506

Now let’s check the new scatter plot… mmm still not the best

scatter <- ggplot(gene_loc2, aes(x = End-Start, y = Length, group=Chr, color=Chr)) +
  geom_point()
scatter

the gray background is annoying… remove it!

scatter <- ggplot(gene_loc2, aes(x = End-Start, y = Length, group=Chr, color=Chr)) +
  geom_point() +
  theme_bw()
scatter

recap, try a different geometry .. by yourself!

box1 <- ggplot(gene_loc2, aes(x = Chr, y = Length, group=Chr, color=Chr)) +
  geom_boxplot() +
  theme_bw()
box1

adjust the axes

scatter <- ggplot(gene_loc2 ,aes(x = End-Start, y = Length, group=Chr, color=Chr)) +
  geom_point() +
  theme_bw() +
  xlim(0, 2500)+ ylim(0, 10000)
scatter

## Warning: Removed 392 rows containing missing values or values outside the scale range
## (`geom_point()`).

where are the green dots?

scatter3 <- ggplot(gene_loc2 ,aes(x = End-Start, y = Length, group=Chr, color=Chr)) +
  geom_point(alpha = 0.7, size =0.5) +
  theme_bw() +
  xlim(0, 2500)+ ylim(0, 10000)
scatter3

## Warning: Removed 392 rows containing missing values or values outside the scale range
## (`geom_point()`).

Did it change? compare the plots side-by-side

ggarrange(scatter, scatter3,
          labels = c("A", "B"),
          ncol = 2, nrow = 1)

## Warning: Removed 392 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 392 rows containing missing values or values outside the scale range
## (`geom_point()`).

transformed the axes.. Thats better.. isn’t it?

trans_scatter <- scatter +
  scale_x_log10("End-Start") +
  scale_y_log10("Gene length") +
  theme_minimal()

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

trans_scatter

You want to add the regression lines.. So, lets do a multiple regression

scatter1 <- ggplot(gene_loc2, aes(x = log_EndStart, y = log_length, color=Chr)) +
  geom_point() +
  theme_bw() +
  geom_smooth(method=lm,  se=FALSE)
scatter1

## `geom_smooth()` using formula = 'y ~ x'

We can’t see the lines clearly. Can you think of a solution?

scatter2 <- ggplot(gene_loc2, aes(x = log_EndStart, y = log_length, color=Chr)) +
  geom_point(size =1, alpha = 0.2) +
  geom_smooth(method=lm, se=FALSE) +
  theme_bw()
scatter2

## `geom_smooth()` using formula = 'y ~ x'

Can you put them together in the same graph to compare?

ggarrange(scatter1, scatter2,
          labels = c("A", "B"),
          ncol = 2, nrow = 1)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

Now, lets add some numerical values to the graph. Like R^2

scatter <- ggplot(gene_loc2, aes(x = log_EndStart, y = log_length, color = Chr))+
  geom_point() +
  theme_bw() +
  geom_smooth(method = lm, se = FALSE)+
  ggpubr::stat_cor()
scatter

## `geom_smooth()` using formula = 'y ~ x'

Now, lets add some numerical values to the graph. Linear equation

scatter <- ggplot(gene_loc2, aes(x = log_EndStart, y = log_length, color = Chr))+
  geom_point() +
  geom_smooth(method = lm, se = FALSE)+
  ggpubr::stat_regline_equation()
scatter

## `geom_smooth()` using formula = 'y ~ x'

Your boss wants to see the lines in different plots!
multiple regression with equation and r2 different plots

ml_scatter <- ggscatter(gene_loc2, x="log_EndStart", y="log_length",
                        color = "Chr", palette = "jco",
                        add = "reg.line", add.params = list(color = "black")) +
  facet_wrap(~Chr) +
  stat_cor(label.y = 4.4) +
  stat_regline_equation(label.y = 4.2)
ml_scatter

labeling a point in a scatterplot..

scatter <- ggplot(gene_loc2 ,aes(x = End-Start, y = Length, group=Chr, color=Chr)) +
  geom_point()
scatter

that gene in the corner looks interesting!! What gene is it?

scatter <- ggplot(gene_loc2, aes(x = End-Start, y = Length, group=Chr, color=Chr)) +
  geom_point()+
  geom_text(label=gene_loc2$Geneid, size = 2, color="black")
scatter

second example!! Labeling point and adding confidence interval to a regression.

a <- gene_loc %>%
  group_by(Chr) %>%
  summarize(meanLength = mean(Length), numGenes = n())
head(a)

## # A tibble: 6 × 3
##   Chr   meanLength numGenes
##   <fct>      <dbl>    <int>
## 1 chr1       2258.     5363
## 2 chr2       2304.     4047
## 3 chr3       2382.     3101
## 4 chr4       2109.     2563
## 5 chr5       2188.     2859
## 6 chr6       2124.     2905

scatter2 <- ggplot(a, aes(x = numGenes, y = meanLength)) +
  geom_point()+
  theme_bw()
scatter2

which chromosome is represented by which point?

scatter2 <- ggplot(a, aes(x = numGenes, y = meanLength)) +
  geom_point()+
  theme_bw()+
  geom_text(label=a$Chr, size = 2, color="black")
scatter2

geom_text_repel is a better function!

scatter2 <- ggplot(a, aes(x = numGenes, y = meanLength)) +
  geom_point()+
  theme_bw()+
  geom_text_repel(aes(label = Chr), color="red", segment.color="blue")
scatter2

add confidence interval

scatter2 <- ggplot(a, aes(x = numGenes, y = meanLength)) +
  geom_point()+
  theme_bw()+
  geom_text_repel(aes(label = Chr), color="red", segment.color="blue")+
  geom_smooth(method = loess, color = "lightblue", alpha = 0.1)
scatter2

## `geom_smooth()` using formula = 'y ~ x'

Part3: Heatmap

Data import
Reading in a subset of the gene count data | Chr22

highvar_genes <- read_tsv("chr22-gene-logcpm.txt")

## Rows: 901 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (5): Geneid, Sample, Source, Stage, Tissue
## dbl (1): Logcpm
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

highvar_genes_df <- highvar_genes %>%
  select(Geneid, Sample, Logcpm) %>%
  spread(Sample, Logcpm) %>%
  column_to_rownames(var="Geneid") %>%
  as.data.frame()

using pheatmap

library(pheatmap); library(RColorBrewer)

pheatmap(highvar_genes_df, cluster_rows=F, cluster_cols=F)

pheatmap(highvar_genes_df)

pheatmap(highvar_genes_df,
         color=brewer.pal(n=9, name="YlGnBu"), # changing cell colors
         border_color="grey90") # changing border colors

OTHER WAYS to generate heatmaps
Using heatmap3

library(heatmap3)

heatmap3(highvar_genes_df)

heatmap3(highvar_genes_df,
         col=brewer.pal(n=9, name="YlGn")) # go green!

Heatmap using ggplot and geom_tile
works with tidy data format

highvar_genes$Sample <- factor(highvar_genes$Sample)

ggplot(data=highvar_genes,
       aes(x=Sample, y=Geneid, fill=Logcpm)) +
  geom_tile(size=0.3) + ## to create heatmap
  scale_fill_gradient(low="white", high="darkred") + # color spec
  #scale_fill_distiller(palette = "YlGnBu") + # if using RColorBrewer
  scale_x_discrete(position="top") + # legend position
  theme_minimal() + #coord_flip() +
  theme(axis.text.x=element_text(angle=90,hjust=0,vjust=0.5))

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

can use hclust for clustering
can use ggdendro to add dendrograms

Part4: More on Combining multiple plots into one

#install.packages("ggsci")
#install.packages("tidyverse")
#install.packages("ggpubr")
#install.packages("ggrepel")
#install.packages("cowplot")
#install.packages("gridExtra")

library(ggsci)
library(tidyverse)
library(ggpubr)
library(ggrepel)
library(cowplot)

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggpubr':
## 
##     get_legend

## The following object is masked from 'package:lubridate':
## 
##     stamp

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

Read the data
We’ll use the plots you’ve already created in part3: box1, ml_scatter, scatter2

## Data Import
gene_loc <- read.table("GSE69360.gene-locations.txt",
                       header = T)

## Plotting the data
scatter <- ggplot(gene_loc, aes(x=End-Start, y=Length, group=Chr, color=Chr)) +
  geom_point()
scatter

It is hard to visualize the entire data.
Let’s pretend we are only interested in a small set of chromosomes.
Let’s subset the data and add a few variables!

target <- c("chrX", "chrY", "chrM", "chr17")
gene_loc2 <- filter(gene_loc, Chr %in% target)

log_EndStart <- log10(gene_loc2$End-gene_loc2$Start)
log_length <- log10(gene_loc2$Length)
gene_loc2$log_length <- log_length
gene_loc2$log_EndStart <- log_EndStart
head(gene_loc2)

##            Geneid   Chr  Start    End Strand Length log_length log_EndStart
## 1 ENSG00000273288 chr17   4961   5048      -     88   1.944483     1.939519
## 2 ENSG00000272636 chr17   5810   6168      -   1480   3.170262     2.553883
## 3 ENSG00000273172 chr17  33615  34249      -   1185   3.073718     2.802089
## 4 ENSG00000181031 chr17  62293  63714      -   5953   3.774736     3.152594
## 5 ENSG00000262920 chr17 171183 171422      +    432   2.635484     2.378398
## 6 ENSG00000262061 chr17 180996 183279      +   2284   3.358696     3.358506

– Combine boxplot and regression plot previously created

### Boxplot
box1 <- ggplot(gene_loc2, aes(x = Chr, y = Length,
                              group=Chr, color=Chr)) +
  geom_boxplot() +
  xlab("Chr")+
  theme_bw()

box1 <- box1 + scale_color_jco()+
  theme(legend.position = "none")                 # remove legend
#box1 + scale_fill_discrete(name = "Chromosome")  #rename legend

Scatterplot + regression line

ml_scatter <- ggscatter(gene_loc2, x = "log_EndStart", y = "log_length",
                        color = "Chr", palette = "jco",
                        add = "reg.line", add.params = list(color = "black"),    # customize regression line
                        fullrange = TRUE) +
  facet_wrap(~Chr) +
  stat_cor(label.y = 4.4) +
  stat_regline_equation(label.y = 4.2)
ml_scatter

arrange facet grid as 1x4

ml_scatter <- ml_scatter + facet_grid(cols = vars(Chr))

#Arranging boxplot and scatterplot on one page
## we’ll use the function ggarrange()[in ggpubr], which is a wrapper around the function plot_grid() [in cowplot package].
## Compared to the standard function plot_grid(), ggarange() can arrange multiple ggplots over multiple pages.
## use either

fig <- ggarrange(ml_scatter, box1 + rremove ("x.text"),
                  labels = c("A", "B"),
                  ncol =1, nrow = 2,
                 common.legend = TRUE)
fig

Annotate the arranged figure

annotate_figure(fig,
                             top = text_grob("Visualizing gene length", color = "red", face = "bold", size = 14),  #grob -- graphical object
                             bottom = text_grob("Data source: \n gse69360 data set", color = "blue",         #customize text
                                                hjust = 1, x = 1, face = "italic", size = 10),             # horizontal justification: 0= left align, 1= right align
                             left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90),   # rot = angle to rotate the text
                             right = "I'm done, thanks :-)!",
                             fig.lab = "Figure 1", fig.lab.face = "bold"
)

Adding descriptive text
Text to be added

text <- paste("gse69360 data set is a resource of ribosomal",
              "RNA-depleted RNA-Seq data from different normal",
              "adults and fetal human tissues. The dataset was first",
              "published in https://www.nature.com/articles/sdata201563", sep = " ")
text.p <- ggparagraph(text = text, face = "italic", size = 11, color = "black")

final_plt <- ggarrange(ml_scatter, box1, text.p + rremove ("x.text"),
                 labels = c("A", "B"),
                 ncol =1, nrow = 3,
                 common.legend = TRUE)
final_plt

Export plot

ggsave("mean_lth_viz.pdf", width = 5, height = 5, units = "in")

Place scatterplot together with density plot
Scatter plot colored by groups (“Chr”)

sp <- gene_loc2 %>%
  ggscatter("log_EndStart", "log_length",
            color = "Chr", palette = "jco",
            size = 1, alpha = 0.2)+
  border()

Marginal density plot of x (top panel) and y (right panel)

xplot <- gene_loc2 %>%
  ggdensity("log_EndStart", fill = "Chr", palette = "jco")

yplot <- gene_loc2 %>%
  ggdensity("log_length", fill = "Chr", palette = "jco")+
  rotate()

Cleaning the plots

yplot <- yplot + clean_theme()
xplot <- xplot + clean_theme()

Arranging the plot

fig_2 <- ggarrange(xplot, NULL, sp, yplot,
          ncol = 2, nrow = 2,  align = "hv",
          widths = c(2, 1), heights = c(1, 2),
          common.legend = TRUE)

Annotating the plot

scat_density_plt <- annotate_figure(fig_2,
                             top = text_grob("Scatter plot with marginal density", color = "red", face = "bold", size = 14),  #grob -- graphical object
                             bottom = text_grob("Data source: \n gse69360 data set", color = "blue",         #customize text
                                                hjust = 1, x = 1, face = "italic", size = 10),             # horizontal justification: 0= left align, 1= right align
                             left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90),   # rot = angle to rotate the text
                             right = "I'm done, thanks :-)!",
                             fig.lab = "Figure 1", fig.lab.face = "bold"
)

scat_density_plt

ggsave("marginal_distribution.pdf", width = 5, height = 5, units = "in")

Insert a table into a plot
Regression plot summary data with labels & confidence interval made in part 2 using code below.

a <- gene_loc %>%
  group_by(Chr) %>%
  summarize(meanLength = mean(Length), numGenes = n())
head(a)

## # A tibble: 6 × 3
##   Chr   meanLength numGenes
##   <chr>      <dbl>    <int>
## 1 chr1       2258.     5363
## 2 chr10      2160.     2260
## 3 chr11      2218.     3208
## 4 chr12      2342.     2818
## 5 chr13      1875.     1217
## 6 chr14      1892.     2244

scatter2 <- ggplot(a, aes(x = numGenes, y = meanLength)) +
  geom_point()+
  theme_bw()+
  geom_text_repel(aes(label = Chr), color="red", segment.color="blue")+
  geom_smooth(method = loess, color = "lightblue", alpha = 0.1)
scatter2

## `geom_smooth()` using formula = 'y ~ x'

Compute descriptive statistics by groups

stable <- gene_loc2 %>%
  desc_statby(measure.var = "Length",             #Column containing variable to be sumarized
              grps = "Chr")                       # Group
stable <- stable[, c("Chr", "length", "mean", "sd")]

Summary table plot, medium blue theme

stable.p <- ggtexttable(stable, rows = NULL,
                        theme = ttheme("mBlue"))

Arranging the plot

fig_3 <- ggarrange(scatter2, stable.p, text.p + rremove ("x.text"),
                   ncol =1, nrow = 3,
                   heights = c(1, 0.5, 0.2),
                   common.legend = TRUE)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fig_3

## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Place the table within the plot using annotate_custom in ggplot

fig_4 <- scatter2 + annotation_custom(ggplotGrob(stable.p),
                                      xmin = 2000, ymax = 1500,
                                      xmax = 5000)

Annotating the plot

final_scatter2 <- annotate_figure(fig_4,
                                  top = text_grob("Scatter plot with summary table", color = "red", face = "bold", size = 14),  #grob -- graphical object
                                  bottom = text_grob("Data source: \n gse69360 data set", color = "blue",         #customize text
                                                     hjust = 1, x = 1, face = "italic", size = 10),             # horizontal justification: 0= left align, 1= right align
                                  left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90),   # rot = angle to rotate the text
                                  right = "I'm done, thanks :-)!",
                                  fig.lab = "Figure 1", fig.lab.face = "bold"
)

## `geom_smooth()` using formula = 'y ~ x'

final_scatter2

Export plot

#Export plot
ggsave("mean_length.pdf", width = 5, height = 5, units = "in")

## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

R-DataViz_2.0

2024-10-15

Introduction

Creating an R Markdown file

ggplot introduction

File Management

Part1: Visualizing tidy data w/ `ggplot`

Reviewing the basics of the Grammar of Graphics

Basic plotting

Barplots & Histograms

A little more about aesthetics: Colors!

Global versus data-level color scales

Customized color scales

Part2: Arrange multiple ggplot objects on a page

Part3: Heatmap

Part4: More on Combining multiple plots into one

R-DataViz_2.0

2024-10-15

Introduction

Creating an R Markdown file

ggplot introduction

File Management

Part1: Visualizing tidy data w/ ggplot

Reviewing the basics of the Grammar of Graphics

Basic plotting

Barplots & Histograms

A little more about aesthetics: Colors!

Global versus data-level color scales

Customized color scales

Part2: Arrange multiple ggplot objects on a page

Part3: Heatmap

Part4: More on Combining multiple plots into one

Part1: Visualizing tidy data w/ `ggplot`